The theme for CIMdata’s 2019 PLM Market & Industry Forum series is “Augmented Intelligence: Applications Across the Product Lifecycle.” CIMdata believes that the work done to date on this topic barely scratches the surface of what is possible. With this in mind let’s take a look at how an augmented intelligence capability should be developed for product lifecycle management (PLM) as part of the enterprise digital transformation journey.
CIMdata defines PLM as a strategic business approach that involves orchestrating the creation, maintenance, and reuse of intellectual assets, resulting from the innovative efforts of manufacturers, suppliers, and startups. These intellectual assets often engender large volumes of heterogenous data, leading to the challenge of storage, communication, analysis, reporting, as well as, extracting intelligence from the data. Due to increased connectivity, driven largely by the Internet of Things (IoT), between the products and the enterprise systems used to develop them, the data volumes are growing at an explosive rate. Nevertheless, the technological advances for dealing with petabytes of structured and unstructured data are also surging, resulting in the expansion of the frontiers of analytics, machine learning (ML), and artificial intelligence (AI) in many business disciplines, including PLM.
PLM, as an enterprise wide enabler of innovation, should not only leverage data flows related to the product lifecycle, it should also exploit the data shared with adjacent enterprise systems such as enterprise resource planning (ERP), manufacturing execution system (MES), customer relationship management (CRM), etc. In the past, PLM-related data flows have been used to drive product and process innovation, and operational excellence, mostly based on human-intuition. With the increasingly fierce global competition to bring better and cheaper products and product-service combinations to the market faster, it is no longer possible to rely on human-intuition alone, and other methods, such as advanced analytics, ML, and AI, must be applied for uncovering the intelligence hidden in plain-sight, and in the counterintuitive corners of the enterprise data.
The digital transformation of businesses must not only digitalize the product and process related assets for realizing innovative products and product-service combinations at a competitive pace, it must also follow a roadmap for attaining the so-called prescriptive state of AI-level analytics, where an understanding of what has happened, why it has happened, and a variety of what-could-happen analyses are possible within the agile development cycles. The prescriptive state provides the innovation engines of businesses with the most meaningful alternatives in any decision-making situation, manifesting the augmented intelligence capability across the enterprise. The journey of digital transformation includes different states of maturity such as descriptive, diagnostic, predictive, and finally prescriptive. The extent to which the vast PLM-related enterprise data, can be leveraged based on the application of advanced analytics, ML, and AI to generate actionable-insights built directly into the work-flow applications, determines the maturity level of the digital transformation of the enterprise.
Several analytics-, ML-, and AI-related proof-of-concept (POC) projects are being undertaken by manufacturing businesses today in distinct areas of PLM, e.g., requirements, service, parts-classification, etc. However, using these POC projects to help build a strategy for digital transformation around PLM to leverage ML and AI for driving revenue and operational excellence requires practical applications of augmented intelligence to create tangible competitive advantage.
In the session on Intelligence for Product Lifecycle Management at CIMdata’s 2019 PLM Market & Industry Forum, CIMdata will show how an augmented intelligence capability should be developed for PLM as part of the enterprise digital transformation journey, with an assessment of the maturity in the industry and the solutions available. With these talks CIMdata hopes to kickstart the conversation on engineering intelligent systems that can serve our needs and extend our capabilities.
2019 PLM Market & Industry Forum events will take place in Ann Arbor MI on April 4; Frankfurt, Germany on April 11; Pune, India on April 15; Beijing, China on April 19, and Tokyo, Japan on April 24.
Key takeaways from the discussion on this topic will include:
- Product lifecycle management (PLM) is the innovation engine of product manufacturing businesses as it helps orchestrate the creation, maintenance, and reuse of digital assets that are central to developing new products and product-service combinations.
- The Internet of Things (IoT) has fostered connectivity in PLM resulting in unprecedented growth of product-related structured and unstructured enterprise data that can augment human intelligence through appropriate analysis.
- By using the big-data flowing within PLM and between PLM and adjacent enterprise solutions, one can leverage a plethora of new PLM-related artificial intelligence (AI) applications to bring better products to markets faster and cheaper.
Let us know your thoughts on this topic by sharing them in the comments section of this blog!
Those wanting to learn more about this topic might be interested in: a recent CIMdata webinar, “PLM at the Core of Analytics-Driven Innovation,” which is available for free download here and a recent white paper, “The Impact of Technology on Service,” which is also available for download from the CIMdata website here.